In [1]:
import os
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import chart_studio.plotly as py
from IPython.display import IFrame
from datetime import datetime
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode()
%matplotlib inline
In [2]:
confirmed_cases_path = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv"
deaths_path = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv"
cured_path = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv"
In [3]:
confirmed_cases = pd.read_csv(confirmed_cases_path)
confirmed_cases.head()
Out[3]:
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 NaN Thailand 15.0000 101.0000 2 3 5 7 8 8 ... 75 82 114 147 177 212 272 322 411 599
1 NaN Japan 36.0000 138.0000 2 1 2 2 4 4 ... 701 773 839 825 878 889 924 963 1007 1086
2 NaN Singapore 1.2833 103.8333 0 1 3 3 4 5 ... 200 212 226 243 266 313 345 385 432 455
3 NaN Nepal 28.1667 84.2500 0 0 0 1 1 1 ... 1 1 1 1 1 1 1 1 1 2
4 NaN Malaysia 2.5000 112.5000 0 0 0 3 4 4 ... 197 238 428 566 673 790 900 1030 1183 1306

5 rows × 65 columns

In [4]:
deaths_data = pd.read_csv(deaths_path)
deaths_data.head()
Out[4]:
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 NaN Thailand 15.0000 101.0000 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1
1 NaN Japan 36.0000 138.0000 0 0 0 0 0 0 ... 19 22 22 27 29 29 29 33 35 40
2 NaN Singapore 1.2833 103.8333 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 2 2
3 NaN Nepal 28.1667 84.2500 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
4 NaN Malaysia 2.5000 112.5000 0 0 0 0 0 0 ... 0 0 0 0 2 2 2 3 4 10

5 rows × 65 columns

In [5]:
recovered_cases = pd.read_csv(cured_path)
recovered_cases.head()
Out[5]:
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 ... 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 NaN Thailand 15.0000 101.0000 0 0 0 0 2 2 ... 35 35 35 35 41 42 42 42 42 44
1 NaN Japan 36.0000 138.0000 0 0 0 0 1 1 ... 118 118 118 144 144 144 150 191 232 235
2 NaN Singapore 1.2833 103.8333 0 0 0 0 0 0 ... 97 105 105 109 114 114 114 124 140 144
3 NaN Nepal 28.1667 84.2500 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1
4 NaN Malaysia 2.5000 112.5000 0 0 0 0 0 0 ... 26 35 42 42 49 60 75 87 114 139

5 rows × 65 columns

Let us see the cumulative growth of number of cases per each day over the entire world

In [6]:
days_columns = confirmed_cases.columns[4:]
In [7]:
world_cases_growth = confirmed_cases[days_columns].sum(axis = 0).reset_index()
world_cases_growth.columns = ['Date', 'Count']
In [8]:
def isweekend(date):
    return datetime.isoweekday(pd.to_datetime(date)) > 5
world_cases_growth['isweekend'] = world_cases_growth['Date'].apply(isweekend).astype(int)
In [9]:
plt.rcParams['figure.figsize'] = [20, 10]
plt.stem(world_cases_growth['Date'], world_cases_growth['Count'], '--ro')
plt.plot(world_cases_growth['Date'], world_cases_growth['Count'], '--bo')
plt.title("Spread of virus per each day")
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.yticks(world_cases_growth['Count'])
plt.show()
Sudden spike can be seen from 12-Feb to 13-Feb almost 15000 cases were identified in just a single day . But most of them could be suspected cases.

Delta difference from day to day confirmed cases

In [10]:
world_cases_growth['delta_confirmed'] = world_cases_growth['Count'].sub(world_cases_growth['Count'].shift())
world_cases_growth.loc[0, 'delta_confirmed'] = world_cases_growth.loc[0, 'Count']
In [11]:
plt.rcParams['figure.figsize'] = [20,10]
plt.stem(world_cases_growth['Date'], world_cases_growth['delta_confirmed'], '--yo', color = world_cases_growth['isweekend'])
plt.plot(world_cases_growth['Date'], world_cases_growth['delta_confirmed'], '--ro')
for i in range(world_cases_growth.shape[0]):
    plt.text(world_cases_growth.loc[i, 'Date'], world_cases_growth.loc[i, 'delta_confirmed']+2, 
             world_cases_growth.loc[i,'delta_confirmed'].astype(int))
plt.title("Delta new cases of to Covid-19 identified on each day")
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.show()

C:\Users\Amey\AppData\Roaming\Python\Python36\site-packages\matplotlib\cbook\deprecation.py:107: MatplotlibDeprecationWarning:

stem() got an unexpected keyword argument 'color'. This will raise a TypeError in future versions.

Parallelly let us compare the active cases to # of deaths on each day

In [12]:
world_cases_growth['deaths'] = deaths_data[days_columns].sum(axis = 0).reset_index(drop = True)
In [13]:
plt.rcParams['figure.figsize'] = [20,10]
plt.stem(world_cases_growth['Date'], world_cases_growth['deaths'], '--yo')
plt.plot(world_cases_growth['Date'], world_cases_growth['deaths'], '--ro')
for i in range(world_cases_growth.shape[0]):
    plt.text(world_cases_growth.loc[i, 'Date'], world_cases_growth.loc[i, 'deaths']+50, 
             world_cases_growth.loc[i,'deaths'])
plt.title("Growth of death toll due to Covid-19 per each day")
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.show()

But the above representation is a cumulative sum of previous day count and newly added count, We can look at the delta change for every day.

In [14]:
world_cases_growth['delta_deaths'] = world_cases_growth['deaths'].sub(world_cases_growth['deaths'].shift())
world_cases_growth.loc[0, 'delta_deaths'] = world_cases_growth.loc[0, 'deaths']
In [15]:
plt.rcParams['figure.figsize'] = [20,10]
plt.stem(world_cases_growth['Date'], world_cases_growth['delta_deaths'], '--yo')
plt.plot(world_cases_growth['Date'], world_cases_growth['delta_deaths'], '--ro')
for i in range(world_cases_growth.shape[0]):
    plt.text(world_cases_growth.loc[i, 'Date'], world_cases_growth.loc[i, 'delta_deaths']+2, 
             world_cases_growth.loc[i,'delta_deaths'].astype(int))
plt.title("# of deaths due to Covid-19 per each day")
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.show()

Let us also see how many cases were cured on daily basis

In [16]:
world_cases_growth['recovered'] = recovered_cases[days_columns].sum(axis = 0).reset_index(drop = True)
In [17]:
plt.rcParams['figure.figsize'] = [20,10]
plt.stem(world_cases_growth['Date'], world_cases_growth['recovered'], '--yo')
plt.plot(world_cases_growth['Date'], world_cases_growth['recovered'], '--go')
for i in range(world_cases_growth.shape[0]):
    plt.text(world_cases_growth.loc[i, 'Date'], world_cases_growth.loc[i, 'recovered']+50, 
             world_cases_growth.loc[i,'recovered'])
plt.title("# of people that recovered from Covid-19 virus")
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.show()
In [18]:
plt.rcParams['figure.figsize'] = [20, 10]
plt.figure(1)
plt.subplot(311)
plt.plot(world_cases_growth['Date'], world_cases_growth['Count'], '--bo')
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.legend()

plt.subplot(312)
plt.plot(world_cases_growth['Date'], world_cases_growth['deaths'], '--ro')
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.legend()

plt.subplot(313)
plt.plot(world_cases_growth['Date'], world_cases_growth['recovered'], '--go')

plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.legend()
plt.show()

Stacked Bar plot of confirmed, recovered and death counts on daily bases

In [19]:
p1 = plt.bar(world_cases_growth['Date'], world_cases_growth['Count'], color = 'yellow')
p2 = plt.bar(world_cases_growth['Date'], world_cases_growth['recovered'], color='g')
p3 = plt.bar(world_cases_growth['Date'], world_cases_growth['deaths'], color='r')
plt.xticks(world_cases_growth['Date'], rotation = 90)
plt.legend([p1[0], p2[0], p3[0]], ('Confirmed', 'Recovered', 'Deaths'))
plt.xlabel("Date")
plt.title("Stacked diagram of COVID-19 cases")
Out[19]:
Text(0.5,1,'Stacked diagram of COVID-19 cases')
In [20]:
world_cases_growth['deaths'].values[-1]/world_cases_growth['Count'].values[-1]
Out[20]:
0.043553452099239484

Increase in the trend of people recovering is a very good sign and considering the growth of confirmed cases and # of deaths, mortality rate is still as low as 4.3%

Let us now see the growth of virus in each country

In [21]:
con_cases_growth = confirmed_cases.groupby('Country/Region')[days_columns].agg(np.sum).reset_index()
con_cases_growth.head()
Out[21]:
Country/Region 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 ... 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 Afghanistan 0 0 0 0 0 0 0 0 0 ... 7 11 16 21 22 22 22 24 24 40
1 Albania 0 0 0 0 0 0 0 0 0 ... 33 38 42 51 55 59 64 70 76 89
2 Algeria 0 0 0 0 0 0 0 0 0 ... 26 37 48 54 60 74 87 90 139 201
3 Andorra 0 0 0 0 0 0 0 0 0 ... 1 1 1 2 39 39 53 75 88 113
4 Angola 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 1 2 2

5 rows × 62 columns

In [22]:
plt.show()

As per the latest data lets see what is the severity of cases in each country

In [23]:
import requests
html_response = requests.get("https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports?_pjax=%23js-repo-pjax-container")
In [24]:
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_response.content, "html.parser")
In [25]:
a_tags = list(map(lambda x : x.get('href'), soup.findAll(name = 'a', attrs={'class' : 'js-navigation-open '})))
In [26]:
repo_path = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/"+os.path.basename(a_tags[-2])
repo_path
Out[26]:
'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-22-2020.csv'
In [27]:
# daily_data = "csse_covid_19_data\\csse_covid_19_daily_reports"
# daily_data_dir = os.path.join(os.path.dirname(os.getcwd()), daily_data)
# daily_data_files = glob.glob(daily_data_dir+'/*.csv')[-1]
daily_data_files = repo_path
In [28]:
latest_data = pd.read_csv(daily_data_files)
latest_data.head()
Out[28]:
Province/State Country/Region Last Update Confirmed Deaths Recovered Latitude Longitude
0 Hubei China 2020-03-22T09:43:06 67800 3144 59433 30.9756 112.2707
1 NaN Italy 2020-03-22T18:13:20 59138 5476 7024 41.8719 12.5674
2 NaN Spain 2020-03-22T23:13:18 28768 1772 2575 40.4637 -3.7492
3 NaN Germany 2020-03-22T23:43:02 24873 94 266 51.1657 10.4515
4 NaN Iran 2020-03-22T14:13:06 21638 1685 7931 32.4279 53.6880
In [29]:
latest_data['Province/State'] = latest_data['Province/State'].fillna(latest_data['Country/Region'])
latest_data['active'] = latest_data['Confirmed'] - (latest_data[['Deaths', 'Recovered']].sum(axis = 1))
In [30]:
agg_latest_data = latest_data.groupby('Country/Region')['Confirmed', 'Deaths', 'Recovered', 'active'].agg([np.sum]).reset_index()
agg_latest_data.columns = agg_latest_data.columns.droplevel(1) 
agg_latest_data.head()
Out[30]:
Country/Region Confirmed Deaths Recovered active
0 Afghanistan 40 1 1 38
1 Albania 89 2 2 85
2 Algeria 201 17 65 119
3 Andorra 113 1 1 111
4 Angola 2 0 0 2
In [31]:
agg_latest_data = agg_latest_data.loc[(agg_latest_data[['Confirmed', 'Deaths', 'Recovered']] > 0).all(axis=1), :].reset_index(drop = True)
In [32]:
manual_dict = {'mainland china' : 'CHN', 'north macedonia' : 'MKD', 'palestine' : 'PSE', 
               'saint barthelemy' : 'FRA', 'south korea' : 'KOR', 'st. martin' : 'PYC', 
               'uk' : 'GBR', 'us' : 'USA', 'vatican city': 'ITA'}
def get_country_code(series):
    df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')
    df2 = pd.read_csv('countryCodes.csv', sep = '\t')
    country_code_dict = {row[1][0].lower() : row[1][2] for row in df.iterrows()}
    added_dict = {row[1][0].lower() : row[1][2] for row in df2.iterrows()}
#     print(country_code_dict)
    res = []
    for key in series:
        if key.lower() in country_code_dict:
            res.append(country_code_dict.get(key.lower(), None))
        elif key.lower() in added_dict:
            res.append(added_dict.get(key.lower(), None))
        else:
            res.append(manual_dict.get(key.lower(), None))
    return res
In [33]:
agg_latest_data['Code'] = get_country_code(agg_latest_data['Country/Region'])
In [34]:
from scipy.stats import rankdata
In [35]:
fig = px.scatter_geo(agg_latest_data, locations='Code', color=rankdata(agg_latest_data['active'])/agg_latest_data.shape[0], 
                     size = np.log(agg_latest_data['active']),
                     hover_data = ['Country/Region', 'active'], 
                     projection="natural earth", color_continuous_scale = 'YlOrRd',
                   title="Count of Active COVID-19 cases in each country")
fig.update_geos(resolution=110,
    showcountries=True, showcoastlines=False,)
# fig.show()
# fig.write_html("images/worldplot.html")
iplot(fig, filename='images/worldplot')
In [36]:
fig = px.choropleth(agg_latest_data, locations='Code', color=rankdata(agg_latest_data['active'])/agg_latest_data.shape[0], 
                     hover_data = ['Country/Region', 'active'], 
                     projection="natural earth", color_continuous_scale = 'YlOrRd',
                   title="Count of Active COVID-19 cases in each country")
fig.update_geos(resolution=110,
    showcountries=True, showcoastlines=False,)
# fig.show()
# fig.write_html("images/worldplot.html")
iplot(fig, filename='images/worldplot')

https://ameyyadav09.github.io/worldplot.html

On every day new people get affected, a part of affected people recover and some people die. Let us see what is the count of active cases in each day.

Province/State wise distribution of Active COVID-19 cases in Each country

In [37]:
fig = px.treemap(latest_data, path = ['Country/Region', 'Province/State'], values = np.log(latest_data['active']+1),
                hover_data=['active'], title = "Province/State wise distribution of Active COVID-19 cases in Each country")
fig.update_layout(width=1000, 
                  height=2500)
iplot(fig, filename='images/treemap_active.html')

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: RuntimeWarning:

divide by zero encountered in log

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: RuntimeWarning:

invalid value encountered in log

https://ameyyadav09.github.io/treemap_active.html

active = confirmed - (recovered + dead)

In [38]:
world_cases_growth['active_cases'] = world_cases_growth['Count'] - (world_cases_growth[['recovered', 'deaths']].sum(axis = 1))
In [39]:
fig = px.scatter(world_cases_growth, x = 'Date', y = 'active_cases', title='Daily count of active cases of COVID 19 through out the world')
# fig.show()
iplot(fig, filename='active_cases')

The number of Active cases have almost doubled with in the span of one week, 55K on 12-Mar and the number went past 120k on 18-Mar

Let us have a look at the list of countries in which Active cases grew. For now we will concentrate on a selected list of countries which have been appearing a lot in News.
In [47]:
selected_countries = ['China', 'Italy', 'France', 'Spain','Germany', 'Iran', 
                      'Korea, South', 'US', 'United Kingdom', 'Switzerland', 'India']
# selected_dates = [1]+list(range(4, confirmed_cases.shape[1]))
selected_dates = [1]+list(range(confirmed_cases.shape[1]-14, confirmed_cases.shape[1]))
In [48]:
confirmed_cases_view = confirmed_cases.iloc[:, selected_dates]
confirmed_cases_view = confirmed_cases_view.loc[confirmed_cases_view['Country/Region'].isin(selected_countries)].groupby('Country/Region').agg([sum]).reset_index(drop=False)
confirmed_cases_view.columns = confirmed_cases_view.columns.droplevel(1)
confirmed_cases_view
Out[48]:
Country/Region 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 China 80860 80887 80921 80932 80945 80977 81003 81033 81058 81102 81156 81250 81305 81397
1 France 1217 1792 2290 2290 3678 4487 4523 6668 7699 9105 10947 12726 14431 16176
2 Germany 1176 1457 1908 2078 3675 4585 5795 7272 9257 12327 15320 19848 22213 24873
3 India 43 56 62 73 82 102 113 119 142 156 194 244 330 396
4 Iran 7161 8042 9000 10075 11364 12729 13938 14991 16169 17361 18407 19644 20610 21638
5 Italy 9172 10149 12462 12462 17660 21157 24747 27980 31506 35713 41035 47021 53578 59138
6 Korea, South 7478 7513 7755 7869 7979 8086 8162 8236 8320 8413 8565 8652 8799 8897
7 Spain 1073 1695 2277 2277 5232 6391 7798 9942 11748 13910 17963 20410 25374 28768
8 Switzerland 374 491 652 652 1139 1359 2200 2200 2700 3028 4075 5294 6575 7245
9 US 583 959 1281 1663 2179 2727 3499 4632 6421 7783 13677 19100 25489 33272
10 United Kingdom 322 384 459 459 802 1144 1145 1551 1960 2642 2716 4014 5067 5741
In [49]:
recovered_cases_view = recovered_cases.iloc[:, selected_dates]
recovered_cases_view = recovered_cases_view.loc[recovered_cases_view['Country/Region'].isin(selected_countries)].groupby('Country/Region').agg([sum]).reset_index(drop=False)
recovered_cases_view.columns = recovered_cases_view.columns.droplevel(1)
recovered_cases_view
Out[49]:
Country/Region 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 China 58804 60181 61644 62901 64196 65660 67017 67910 68798 69755 70535 71266 71857 72362
1 France 12 12 12 12 12 12 12 12 12 12 12 12 12 2206
2 Germany 18 18 25 25 46 46 46 67 67 105 113 180 233 266
3 India 3 4 4 4 4 4 13 13 14 14 15 20 23 27
4 Iran 2394 2731 2959 2959 2959 2959 4590 4590 5389 5389 5710 6745 7635 7931
5 Italy 724 724 1045 1045 1439 1966 2335 2749 2941 4025 4440 4440 6072 7024
6 Korea, South 118 247 288 333 510 510 510 1137 1407 1540 1540 1540 1540 2909
7 Spain 32 32 183 183 193 517 517 530 1028 1081 1107 1588 2125 2575
8 Switzerland 3 3 4 4 4 4 4 4 4 15 15 15 15 131
9 US 7 8 8 12 12 12 12 17 17 0 0 0 0 0
10 United Kingdom 18 19 19 19 19 19 19 21 53 67 67 67 67 67
In [50]:
deaths_data_view = deaths_data.iloc[:, selected_dates]
deaths_data_view = deaths_data_view.loc[deaths_data_view['Country/Region'].isin(selected_countries)].groupby('Country/Region').agg([sum]).reset_index(drop=False)
deaths_data_view.columns = deaths_data_view.columns.droplevel(1)
deaths_data_view
Out[50]:
Country/Region 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 China 3123 3139 3161 3172 3180 3193 3203 3217 3230 3241 3249 3253 3259 3265
1 France 19 33 48 48 79 91 91 148 148 148 243 450 562 674
2 Germany 2 2 3 3 7 9 11 17 24 28 44 67 84 94
3 India 0 0 1 1 2 2 2 2 3 3 4 5 4 7
4 Iran 237 291 354 429 514 611 724 853 988 1135 1284 1433 1556 1685
5 Italy 463 631 827 827 1266 1441 1809 2158 2503 2978 3405 4032 4825 5476
6 Korea, South 53 54 60 66 66 72 75 75 81 84 91 94 102 104
7 Spain 28 35 54 55 133 195 289 342 533 623 830 1043 1375 1772
8 Switzerland 2 3 4 4 11 13 14 14 27 28 41 54 75 98
9 US 22 28 36 40 47 54 63 85 108 118 200 244 307 417
10 United Kingdom 4 6 8 8 8 21 21 56 56 72 138 178 234 282
In [51]:
active_data_view = confirmed_cases_view.iloc[:, 1:] - (recovered_cases_view.iloc[:, 1:]+deaths_data_view.iloc[:, 1:])
active_data_view
Out[51]:
3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20
0 18933 17567 16116 14859 13569 12124 10783 9906 9030 8106 7372 6731 6189 5770
1 1186 1747 2230 2230 3587 4384 4420 6508 7539 8945 10692 12264 13857 13296
2 1156 1437 1880 2050 3622 4530 5738 7188 9166 12194 15163 19601 21896 24513
3 40 52 57 68 76 96 98 104 125 139 175 219 303 362
4 4530 5020 5687 6687 7891 9159 8624 9548 9792 10837 11413 11466 11419 12022
5 7985 8794 10590 10590 14955 17750 20603 23073 26062 28710 33190 38549 42681 46638
6 7307 7212 7407 7470 7403 7504 7577 7024 6832 6789 6934 7018 7157 5884
7 1013 1628 2040 2039 4906 5679 6992 9070 10187 12206 16026 17779 21874 24421
8 369 485 644 644 1124 1342 2182 2182 2669 2985 4019 5225 6485 7016
9 554 923 1237 1611 2120 2661 3424 4530 6296 7665 13477 18856 25182 32855
10 300 359 432 432 775 1104 1105 1474 1851 2503 2511 3769 4766 5392
In [52]:
import plotly.graph_objects as go
In [53]:
fig = go.Figure()
for i in range(confirmed_cases_view.shape[0]):
    fig.add_trace(go.Scatter(x = confirmed_cases_view.columns[1:], y = active_data_view.iloc[i, 1:], 
                             name = confirmed_cases_view.iloc[i, 0]))
fig.update_layout(title = "Spread of COVID-19 in last 14 days in major Countries", height=800, width = 1000)
iplot(fig, filename='country_spread')

The graph shows how much a nation is prepared for an eidemic, clearly Italy was not expecting such massive outbreak. China on the otherhand showed the world that with proper care this Virus can be contained.